3 citations · 3 across the 11 of their papers we have counts for
11 papers
Temporal Pooling Strategies for Training-Free Anomalous Sound Detection with Self-Supervised Audio Embeddings
Kevin Wilkinghoff, Sarthak Yadav, Zheng-Hua Tan
Training-free anomalous sound detection (ASD) based on pre-trained audio embedding models has recently garnered significant attention, as it enables the detection of anomalous soun…
An overview of neural architectures for self-supervised audio representation learning from masked spectrograms
Sarthak Yadav, Sergios Theodoridis, Zheng-Hua Tan
In recent years, self-supervised learning has amassed significant interest for training deep neural representations without labeled data. One such self-supervised learning approach…
AudioMAE++: learning better masked audio representations with SwiGLU FFNs
Sarthak Yadav, Sergios Theodoridis, Zheng-Hua Tan
Masked Autoencoders (MAEs) trained on audio spectrogram patches have emerged as a prominent approach for learning self-supervised audio representations. While several recent papers…
AxLSTMs: learning self-supervised audio representations with xLSTMs
Sarthak Yadav, Sergios Theodoridis, Zheng-Hua Tan
While the transformer has emerged as the eminent neural architecture, several independent lines of research have emerged to address its limitations. Recurrent neural approaches hav…
Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations
Sarthak Yadav, Zheng-Hua Tan
Despite its widespread adoption as the prominent neural architecture, the Transformer has spurred several independent lines of work to address its limitations. One such approach is…
On Permutation Selectors and their Applications in Ad-Hoc Radio Networks Protocols
Jordan Kuschner, Yugarshi Shashwat, Sarthak Yadav +1
Selective families of sets, or selectors, are combinatorial tools used to "isolate" individual members of sets from some set family. Given a set and an element , to iso…
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
Sarthak Yadav, Sergios Theodoridis, Lars Kai Hansen +1
In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-gl…
Learning neural audio features without supervision
Sarthak Yadav, Neil Zeghidour
Deep audio classification, traditionally cast as training a deep neural network on top of mel-filterbanks in a supervised fashion, has recently benefited from two independent lines…
GISE-51: A scalable isolated sound events dataset
Sarthak Yadav, Mary Ellen Foster
Most of the existing isolated sound event datasets comprise a small number of sound event classes, usually 10 to 15, restricted to a small domain, such as domestic and urban sound…
End-to-End Bengali Speech Recognition
Sayan Mandal, Sarthak Yadav, Atul Rai
Bengali is a prominent language of the Indian subcontinent. However, while many state-of-the-art acoustic models exist for prominent languages spoken in the region, research and re…
Frequency and temporal convolutional attention for text-independent speaker recognition
Sarthak Yadav, Atul Rai
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a d…